Computing systems obtain a high degree of functionality by executing software programs. A dataflow network is a directed graph where nodes represent computations and the edges represent data flowing between the nodes. Dataflow networks are ubiquitous in software. They are relatively easy to build, reason about, and provide a degree of parallelism by allowing multiple nodes to execute in parallel.
In typical dataflow systems, execution of the nodes of the dataflow network is driven solely by the availability of the input expected by the nodes. Isolated and side-effect free dataflow networks (such as Kahn process networks) do not read or alter the external state. Conversely, non-isolated dataflow networks (such as Unix pipes) can read and/or alter the external state. Non-isolated networks have a potential of better performance due to their ability to perform in-place updates and avoid data copying. However, unrestricted access to shared mutable state is prone to a data race, a situation where a node can observe an invalid state of an object in memory, while another node is at the same time mutating the state of that object.